Keywords
Forensic Science, Imaging Flow Cytometry, Autofluorescence, Forensic Biology, Forensic DNA Profiling
New methods for processing ‘touch’ or trace biological samples is an ongoing priority for forensic caseworking laboratories. These samples often contain materials from multiple individuals in varying quantities and/or degrees of degradation. Rapid characterization of cellular material before DNA profiling can allow laboratories to screen samples for the presence of multiple contributors or the amount of biological material present.
This dataset contains autofluorescence and morphological profiles of epidermal cell populations analyzed using Imaging Flow Cytometry. The epidermal samples were aged for varying amounts of time prior to analysis. Multiple samples from the same individual were also collected to assess profile variations within and across the contributors.
This data set may be used to investigate variability in epidermal cell populations from different individuals and potential forensic signatures contained within the non-genetic components that comprise touch biological evidence.
Forensic Science, Imaging Flow Cytometry, Autofluorescence, Forensic Biology, Forensic DNA Profiling
One of the most prevalent and challenging types of evidence submitted to forensic laboratories is ‘touch’ biological samples. These are created when one or more individuals handle a substrate and transfer cellular and/or genetic material onto its surface. As touch evidence is often composed of contributions from multiple individuals (e.g. Burrill et al., 2019, 2022), the resulting DNA profiles can be difficult to interpret, leading to delays in processing time and/or loss of the sample’s probative value. Autofluorescence profiling of cell populations may provide a way to rapidly detect when a sample contains cells from multiple individuals. Previous research has shown that epidermal cell populations from different individuals can show distinct variations in the intensity of autofluorescence at specific excitation wavelengths and emission channels (Stanciu et al., 2016). Furthermore, donor-specific autofluorescence signatures can be used to separate cells from different contributors prior to DNA profiling (Philpott et al., 2017). Using these studies as a foundation, we have built a dataset of a range of relevant sample types that have been characterized using Imaging Flow Cytometry (IFC): cell populations collected directly from the palm, as well as swabbings of handled items of known and unknown provenance (i.e. controlled and uncontrolled ‘touch’ samples). IFC collects both autofluorescence and morphological measurements from individual cells within a sample in a high-throughput manner. This can facilitate the development of forensic signatures for rapidly detecting whether an evidentiary sample contains biological deposits from multiple individuals or a single contributor, or to develop front-end methods for separating cells based on morphological or fluorescent properties. As part of this dataset, we included replicate samples from the same individual to assess intra-donor variability of autofluorescence/morphological characteristics, as well as samples aged for varying time intervals before IFC analysis to assess the impact that this may have on the individualizing features of epidermal cells.
All procedures for collecting epidermal cell populations were reviewed and approved by the Virginia Commonwealth University Institutional Review Board (Protocol# HM2000454; Approved 4/3/23). Written informed consent was obtained from all participants. Samples were collected between 4/30/23 and 6/30/24. Samples were collected from participants either by swabbing the palmar surface using a pre-wetted cotton swab (Puritan, P/N) or by having individuals handle the substrate (e.g., a plastic tube or knife handle) for approximately 3 min. Samples were collected from the handled substrates in a similar manner (i.e., surface swabbed with a pre-wetted cotton swab). All swabs were placed in 1mL of sterile H2O and incubated for 15 min at room temperature. During this incubation step, the swabs were agitated every 5 min using a pulse vortex set at 3000 rpm. This facilitated the removal of cells from the cotton matrix of the swab into the solution. Next, the swab was removed and the resulting cell solution was centrifuged at 10,000 × g for 5 min to pellet the cells, decanted to remove the supernatant, and resuspended in 1mL of sterile H2O. This washing step was performed twice. During the final wash step, the cells were resuspended in 50 μL H2O prior to IFC.
Samples were analyzed using the Amnis® Flowsight® imaging flow cytometer (Cytek Biosciences, Dallas, TX, USA) equipped with 405 nm, 488 nm, 642 nm, and 785 nm lasers in a dual channel configuration. The voltages were set to 100 mW, 60 mW, 100 mW, and 6.25 mW, respectively. Images of individual events were captured at 20× magnification in nine fluorescent detector channels, two bright field channels (channels 1 and 9), and a side-scatter channel (channel 6). Excitation lasers and emission channels were configured in a dual-channel arrangement such that the 488 nm and 785 nm excitation lasers were associated with channels 1-6 and the 405 nm and 642 nm excitation lasers were associated with channels 7-12. The emission wavelengths for each detector channel used for autofluorescence data collection were as follows: channel 2 (505–560 nm), channel 3 (560–595 nm), channel 4 (595–642 nm), channel 5 (642-745 nm); (745 nm), channel 7 (435–505); channel 8 (595–560 nm), channel 10 (595–642 nm), channel 11 (642–745 nm), and channel 12 (745–800 nm). The IFC data were saved in a raw image format (. rif). Alpha-numeric codes were used to designate data files from different individuals. The time between sample collection/deposition and IFC analysis is listed for each sample, along with whether the sample was taken by swabbing the individual’s hand or swabbing a substrate surface after handling by the individual.
Samples were obtained from 57 unique individuals and aged between 0-3,650 days with some samples aged to different time points before analysis.
CE conceived the study. CE, AG, and KP designed experiments. AD, GW, and ND conducted this research. CE prepared the first draft of the manuscript. CE, KP, and AG revised the manuscript.
Figshare: Morphological and Autofluorescence Dataset for ‘Touch’ Epidermal Cell Populations, DOI: https://doi.org/10.6084/m9.figshare.27068128.v3 (Ehrhardt et al., 2024).
The project contains the following underlying data:
Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).
The source data files were labeled with an anonymized sample ID number, date of analysis, and time between sample deposition and analysis. Epidermal cell populations taken from touched surfaces are labeled by anonymized ID and the type of substrate (e.g., conical tube, knife handle). Examples of image galleries for individual cells across bright field and fluorescence channels are provided in tiff format.
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Is the rationale for creating the dataset(s) clearly described?
Yes
Are the protocols appropriate and is the work technically sound?
Yes
Are sufficient details of methods and materials provided to allow replication by others?
Yes
Are the datasets clearly presented in a useable and accessible format?
No
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Forensic Science, Forensic Genetics, Touch and Trace DNA Analysis, Crime Scene Investigation, Sexual Assault Forensics, Laboratory Workflow Optimization, Forensic Psychology
Is the rationale for creating the dataset(s) clearly described?
Yes
Are the protocols appropriate and is the work technically sound?
Yes
Are sufficient details of methods and materials provided to allow replication by others?
Yes
Are the datasets clearly presented in a useable and accessible format?
No
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Forensic Biology
Alongside their report, reviewers assign a status to the article:
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Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list:
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